DE4133238A1 - Signal derivation system for real=time evaluation of road surface profile under motor vehicle - has monitors for vertical deflections of wheels and with data reduction signal processing to classify profiles - Google Patents

Abstract

Sensors on the wheel suspensions provide vertical deflection signals which are processed to monitor the suspension dynamics. The processed signals represent a real time profile of the road under the wheel and are compared with reference profiles to classify the signals i.e. data reduction. The classified signals are used to control the active suspension and other systems on the vehicle. The data reduction provides a simplified processing system operating in real time. A simple system monitors the frequency of amplitude peaks above a set level in each sampling period. The system allows active suspension control, improved steering etc. USE/ADVANTAGE - Simplified processing; real-time suspension correction. Dynamic control for regulating, control and monitoring systems, e.g. antilocking or antislip in cars and commercial vehicles.

Description

State of the art

The invention relates to a system for obtaining a roadway Surface representing signal, especially for use in dynamic control, control and / or monitoring systems in passenger and commercial vehicles, according to the type of main saying.

It is known to optimize driving dynamics properties of a motor vehicle regulating, controlling and / or driving dynamics use security systems. This driving dynamics control Monitoring and / or monitoring systems can be divided into two groups the vertical dynamic and the horizontal dynamic driving witness control systems.

Under vertical dynamic vehicle control systems, the Combine chassis control systems. With this undercarriage rain Systems can, for example, change the damping para Skyhook damping, passive damping and / or frequency selector damping a semi-active or partially active suspension control systems are made in such a way that a comfort and  Driving safety for the respective type of road as favorable as possible Vehicle behavior is achieved. Such suspension control systems are for example from WO 90/14 240 (PCT / DE 90/00 343), from which DE-OS 37 38 284 and known from DE patent application P 39 18 735.7.

Horizontal dynamic vehicle control systems can be divided in systems for driving stability monitoring, systems for steering rain tion, in particular for rear axle steering, systems for blocking locking hindrance when braking (ABS) and systems for traction control (ASR). Driving stability monitoring systems monitor the driver witness stability during driving maneuvers, for example during evasive maneuvers and approaching the vehicle boundary. In these, in general mean critical driving situations for driving safety, he will Warned drivers or even interfered with the drive system. At the systems for steering control, especially in steering systems which in addition to the front axle and the rear axle are designed steerable is the vehicle stability, for example in evasive action vern, through targeted steering interventions in the sense of control or Regulation increased.

Especially in connection with the horizontaldyna mentioned above Mix vehicle control systems are those transmitted by the tires Longitudinal and lateral forces of great importance. In case of "bad" driving web quality, that means, for example, with high unevenness amplitudes that in the area of the vertical dynamic wheel frequencies, min the associated dynamic wheel load variations transferable longitudinal and lateral forces of the wheel. As a wheel load variant ation or wheel load fluctuation is the deviation of the wheel load (normal force between tire and road surface) from their static Value.  

In the article by W. Klinkner ("adaptive damping system ADS for Road and driving condition-dependent control of dampers one Vehicle suspension ", VDI Report No. 778, Düsseldorf, 1989) are published by a vertical dynamic vehicle control system damper settings an adaptive chassis depending on static parameters, which describe the character of the road. About who the signals of a body and a wheel acceleration sensor used. In the essay is a frequency-separated Aufbe Riding the recorded bumps in different frequencies proposed areas for which several filters connected in parallel be used. The disadvantage of such a system is the high one Effort on the one hand with regard to the sensors and on the other hand Lich the filter.

The article by D.Konik ("Calculation of unknown input signals from Measuring signals using the example of asperity determination ", at-Automati sierungstechnik 39 (1991) 6 pages 205-210) deals with the Calculation of unknown input signals from the measurement signals of a Systems. The road profile is determined using signals that represent the acceleration of the vehicle body, and signals, which re the relative path between the vehicle body and the wheels present, calculated based on the design of an inverse system.

Method of measuring the distance of the vehicle from the road and the determination of the lane profile with special sensors (Ultra sound, radar, microwave, etc.) are known.

Furthermore, with regard to statistical methods, the References in the following text referenced.

The object of the present invention is to achieve a system one representing the course of the road surface To design signals.  

This object is characterized by the features in claim 1 solved.

Advantages of the invention

In the system according to the invention, information about the driving get train quality to this in a variety of electronic Positioning and control systems for road-bound motor vehicles (passenger cars, Motorcycles, trucks, etc.). This applies in particular to vehicle dynamics control, monitoring and monitoring systems, such as systems for changing the blocking when braking (ABS), traction control systems (ASR), systems for driving stability monitoring, steering control systems or adaptive, semi-active, partially active or fully active chassis. The invention be writes a way to detect a signal that at least approximately the course of the lane profile in real time gives. For this purpose, signals are detected that the relative movements between represent the vehicle body and at least one wheel. Based on these signals, further signals are made according to the invention formed the course of the road surface under each Play the wheel rolling on it in real time. The invention System can be applied to one or more wheels of the vehicle will.

In an advantageous embodiment of the system according to the invention it is provided the real time signals that the lane profile play time (high amount of data) to reduce. For reduction This large amount of data is used to determine parameters which determine the course the road surface or the quality of the road surface characterize (data reduction).  

Another advantageous embodiment of the Sy according to the invention stems is the number of parameters that, as above wrote as results of data reduction are determined in another step to reduce again. He does this according to the invention by classifying these parameters.

In the system according to the invention, data are thus determined reproduce the course of the roadway profile in real time and / or Deliver parameters for the course of the road surface and / or Specify classified parameters. This information about the Ver run of the road surface are now according to the invention the fahrdyna mix control, control and / or monitoring systems supplied. These systems can then follow the course of the road surface be fit by the information obtained according to the invention of be considered that the controller parameters, for example Coefficients and / or threshold values, controller setpoints and / or parts the control algorithm of the vehicle dynamics control, control and / or Monitoring systems are adapted to the course of the road. It must ever but it should be emphasized that the information obtained according to the invention can not be used on the road quality, the Road condition (dry, wet, icy, etc.), but only describe the lane profile or its properties.

Further advantageous refinements of the system according to the invention are marked in the subclaims.

drawing

An embodiment of the system according to the invention is in the Drawings are shown in the description below explained in more detail.  

Description of the execution example

In this exemplary embodiment, the system according to the invention is to be shown on the basis of the block diagram in FIG. 1.

In FIG. 1, the position 100 denotes sensor means. Positions 101 , 102 and 103 represent first, second and third means for signal processing. Signals from the fifth means, which are marked with position 105 , are fed to them. Position 104 shows driving dynamics control, control and / or monitoring systems.

In a first step, first signals are detected in the sensor means 100 , which represent the relative movements between the vehicle body and at least one wheel. These first signals can, for example, be processed signals from sensors that measure the travel and / or the compression speed between the vehicle body and at least one wheel. If the spring deflection is measured, the first signals Xari (t) are present on the output side of the sensor means 100 . If the compression rates are measured in the sensor means 100 , the first signals Xari '(t) are present on the output side. The index i describes the affiliation of the signals to the i-th wheel unit. In addition or as an alternative to the first signals Xari (t) or Xari ′ (t) described above, fourth signals can also be detected which represent the movements of at least one axis of the vehicle, for example the axis acceleration.

The transmission behavior of the first means 101 will be described below with reference to FIG. 2. Positions 201 , 202 , 203 , 204 , 205 and 206 in FIG. 2 show a two-body model for a wheel unit. The wheel is in contact with the lane 204 . The tire rigidity is described as a spring 205 with the spring constant Cr as a model. The combination of the spring 206 and the damper 203 which can be regulated with respect to its damping property thus stands here for the suspension system and / or damping system to be controlled or regulated in a wheel unit. In this embodiment, damper 203 is assumed to be controllable, while the properties of spring 206 are described by a constant value C. With Xa or Xr, the displacement of the vehicle body 201 or the displacement of the wheel is designated, namely the displacement from the equilibrium position when the vehicle is stationary (in the unloaded state). Xe describes the unevenness in the floor. The mass of the vehicle body is labeled Ma and that of the wheel is labeled Mr. The sensor 207 detects the deflection of the wheel unit. The output signals of the transducer 207 are optionally processed and are on the output side of the sensor means 100 ( FIG. 1) as the first signals Xari (t) or Xari '(t).

The vertical dynamic movement state of a wheel unit of a real vehicle is well approximated by the two-body model shown in FIG. 2. With the coordinates and vehicle parameters shown in FIG. 2, the theoretical relationship between the course of the road surface Xe (t) and the distance measured with a suitable sensor 207 between the wheel unit and the vehicle structure is obtained as the formulaic relationship in the Laplace region

with the Laplace variable s, where Mar = Ma + Mr is the sum of the proportional vehicle body mass and the mass of the wheel and Xari is the relative spring deflection Xa-Xr at the i-th wheel unit. For the practical implementation of the chassis control system according to the invention, the relationship ( 4 ) cannot be used favorably, since the required two-fold integration of the spring deflection signal cannot be realized stably in a control unit. A suitable approximation of equation (4) is, however

where the sizes e, w and δ are filter parameters, the example be adapted to the compression movement signals to be evaluated and which ensure stable integration.

The relationship ( 5 ) describes a time-continuous, stable fourth order filter. For implementation in a digital control device, this filter can be discretized using known methods. In this case, the first device 101 has the transmission behavior described in the formula ( 5 ), the detection of the spring deflection or the determined spring deflection being carried out by the first sensor means 100 .

In addition to the representation described here, it is possible to Application of order reduction procedures the fil described here fourth order arrangement (n = 4) to reduce by example wise an approach

is adjusted in the sense of the smallest squares of errors so that the relationship ( 4 ) or the relationship ( 5 ) is optimally approximated in a frequency band under consideration. This further reduces the computing effort required in the control unit.

If in the vehicle under consideration instead of the spring deflection signals Xari spring deflection speed signals Xari 'are available, the procedure described above can be used equally. In this case, relationship ( 4 ) takes the place of relationship

and instead of (6) the relationship

The procedure described can be on one or more wheels (i = 1,2,24666) of the vehicle under consideration. So can for example, only the deflection signals of the two front wheels are taken into account because the roadway assigned to the rear wheels profiles approximate those of the front wheels at a time correspond to the difference T, which results from the center distance L and the Driving speed V according to T = L / V results.

The first means 101 are supplied with the parameters required in the formulas described above by the fifth means 105 , insofar as they are not stored as constant parameters in the first means 101 . In particular, the quantities e, w and δ, which, for example, are adapted to the compression movement signals to be evaluated, are selected accordingly by the fifth means 105 .

On the output side of the first means 101 , the second signals Si (t) are present, which represent the course of the road surface Xe under the wheel that is rolling thereon. These second signals Si (t) can now be fed directly to the vehicle dynamics control, control and / or monitoring systems 104 , since the second signals contain the entire information content about the course of the road surface. In this exemplary embodiment, as will be described in the following, reductions in the amount of data of the second signals are carried out. Depending on the design of the system according to the invention, this leads to more or less detailed information about the current course of the road surface.

In a first step for data reduction, the second signals Si (t) are fed to the second means 102 . Based on the second signals Si (t) or also on the basis of signals from other sensor configurations (e.g. from vertical axis acceleration signals), one or more descriptive parameters Ki (t) suitable for the roadway are determined in the second means 102 . This means that from the time signals, for example the second signals Si (t), (large amount of data) a few parameters are determined which characterize the course of the road surface or the road surface quality (data reduction). The third signals Ki (t) are present on the output side of the second means 102 . Characteristic of the second means 102 is therefore the reduction in the information content of the second signals Si (t).

To obtain the parameters Ki (t) (second signals), the individual different procedures are used, which in following should be presented. These approaches can individually or in combination to form the parameters Ki (t) ver be applied.  

1. (RMS) effective values and / or peak values of the course of the Road surface describing second signals Si (t)

Starting from the second signals Si (t) are statistical Characteristic values such as RMS (root mean squares) effective values, moving RMS RMS values and / or peak values of the second signals Si (t) determined. The absolute values can be used to form the effective value the signals mentioned are each formed analog or digital (Rectification). Subsequent low-pass filtering gives one an estimate of the effective value. Are they taken into account squared signals in addition to or instead of the amount formation, then low pass filtered and then the root determined, so you get estimated RMS RMS values.

For peak value formation, the means the maximum occurred over a selectable time interval Signal amplitude of the variables under consideration (second signals Si (t)) considered. The selectable time interval is in the sense of a sliding time window always track the real time course.

2. Counting method for determining the frequency within specified Signal amplitude classes and selectable time intervals

In this case, a discrete amplitude distribution of the two signals Si (t) by dividing them into amplitude classes taken. Then the frequency of occurrence becomes more appropriate Amplitude values within a defined period (selectable Time interval) determined. The period under consideration is in the Meaning of a sliding time window always the real time course track. Instead of the actual (signed) Amplitude values can alternatively be the amounts of the amplitudes which are used.  

From the discrete amplitude distribution in the sense of a white statistical data in a simple way such as mean, scatter and higher statistical moments be averaged. In the sense of further data reduction, it is in particular also possible given functions of the given Approach type to the shape of the amplitude distribution, for example in the sense of least squares. The so determined Parameters of this function can be used for classification will. The adjustment of a Gaussian distribution is an example here cited from the parameters of the Gaussian distribution of the means worth and the spread follows. Another example is the adapt solution of a polynomial function. The parameters here are the poly nom coefficients or zeros of the polynomial as parameters use.

3. Frequency analysis

In this case there is a spectral distribution of the signal amplitude that of the second signals Si (t) depending on the frequency using known algorithms for frequency analysis (e.g. FFT, Brigham, E.O .: The Fast Fourier Transform., Prentice-Hall, Inc.Englewood Cliffs, Hew Jersey, 1974)) inner half of a specified period (selectable time interval) averages. The period under consideration is the same always follow the real time course.

From the frequency discrete spectral distribution obtained in this way in the sense of further data reduction in a simple way statistical parameters such as mean, scatter and effective value of the second signals Si (t) can be determined. In the sense of further data reduction, it is also possible, in particular given functions of a given approach type to the form of  Spectral distribution, for example, in the sense of the smallest error qua third rate and the parameters of this function determined in this way to use for classification. For example, a ge broken rational function to be adjusted, taking as a parameter for classification the polynomial coefficients or the poles or Zeros of the fractional rational function can be chosen can.

4. Parameter identification procedure

In this case, the second signals Si (t) are combined fold dynamic model, for example in the form of a diff boundary equation, differential equation or a transfer function, a so-called shape filter, identifies which the actually present second signals Si (t) to one or several, hypothetical white production processes Wi (t) (Box, G.E.P., Jenkiens, G.M .: Time Series Analysis-Forecasting and control, Holden-Day, San Francisco, 1976). For identification Such models are numerous parameter identification procedures known (Eykhoff, P .: System Identification, Parameter and State Estimation, Wiley and Sons, London, 1974). For example here least square estimation methods, maximum likelihood estimate methods, correlation methods or FFT-based methods described in the literature (Eykhoff, P .: System Identifi cation, parameter and state estimation, Wiley and Sons, London, 1974 and Astrom, K.J .: Maximum Likelihood and Prediction Error Methods, Automatica (16) 1980, pp.551-574 and Bendat, J.S., Piersol, A.G .: Engineering Application of Correlation and Spectral Analysis, Wiley and Sons, London, 1980).  

In this case, you can use as parameters for the road classification the intensities of the white production processes Wi (t) and the Coefficients of the identified shape filter (dynamic Mo dell) are considered.

By processing the second signals Si (t) in the second means 102 , using the procedure described above, parameters or parameters Ki (t) are obtained which characterize the course of the road surface. The procedures described above can be used individually or in combination to determine the parameters. The choice of the time intervals in the procedures described above can either be fixed or selected depending on variables that influence and / or represent the driving state. These time intervals and further parameters for carrying out the procedures described above are supplied to the second means 102 by the fifth means 105 . In particular, the choice of the time intervals is to be mentioned, which is made depending on the driving speed and / or on the course of the road surface itself.

The third signals Ki (t) are present on the output side of the second means 102 , which represent the parameters determined according to one or more of the above-described procedures. Starting from these third signals Si (t), a further data reduction is carried out in the third means 103 . This is preferably done by classifying the third signals Ki (t). This can be done by comparing the third signals Ki (t) or the determined characteristic values of the road surface with one or more different threshold values. The classification can be expressed as a logical result or numerical value depending on the result of the comparison. In the case of several characteristics, it makes sense to combine the fulfillment of the various associated threshold values with additional logical links (AND, OR, EXCLOSIVE OR or combinations thereof) in the sense of a "cluster formation" and only if the linked or combined in this way is fulfilled Attributes to assign a classification result.

The third means 103 therefore classify the third signals Ki (t) for data reduction in such a way that the third signals Ki (t) are compared with thresholds, the thresholds being fixed or being selected depending on variables which influence the driving state and / or represent. On the output side of the third means 103 , the classified third signals Kli (t) are present as classification results.

These classified third signals Kli (t) are supplied to the dynamic driving control, control and / or monitoring systems 104 . In the context of the vehicle dynamics control, control and / or monitoring systems 104 , the classified third signals Kli (t) of the type are taken into account that the controller parameters, for example the coefficients and / or threshold values, controller setpoints and / or parts of the control algorithm of the vehicle dynamics rule - Control and / or monitoring systems can be changed depending on the classified third signals Kli (t).

Such adaptation of the driving dynamics control, control and monitoring systems 104 to the course of the road surface can also be done by bypassing the third means 103 by means of the third signals Ki (t). In both cases, this means that the respective dynamic driving system is adapted to the road surface in a certain way.

The following systems are to be mentioned as examples of the vehicle dynamics control, control and / or monitoring systems 104 . Combinations of the systems described below can also be used here.

I. Vertical dynamic vehicle control systems

• 1. Chassis control systems:
  Changing the suspension and / or damping parameters example, in the context of skyhook damping, passive damping, frequency selective damping of a semi-active or partially active suspension control system such that a vehicle behavior that is as favorable as possible in terms of comfort and driving safety is achieved for the respective type of road surface.

II. Horizontal dynamic vehicle control systems

• 1. Systems for driving stability monitoring:
  Systems are known which monitor vehicle stability, for example during evasive maneuvers, and warn the driver when approaching the vehicle limit area or even intervene independently in the drive system.
• 2. Steering control systems, especially systems for rear axle steering
  Steering systems are known which increase vehicle stability, for example during evasive maneuvers, through targeted steering interventions in the sense of a control or regulation. In particular, special steering systems are to be mentioned in which, in addition to the front axle, the wheels of the rear axle are also designed to be steerable.
• 3. Anti-lock braking systems (ABS) are from the State of the art known.
• 4. Systems for traction control (ASR) are also from the State of the art known.

In connection with the horizontal dynamic vehicle control systems the longitudinally and laterally transferable from the tire are of great size Importance. With "poor" road quality, for example with ho unevenness amplitudes in the area of the vertical dynamic wheel frequency, reduce the associated dynamic wheel load variations the transferable longitudinal and lateral forces of the wheel. A procedure in the sense of the described system according to the invention ensures that the behavior through appropriate adaptation measures of the horizontal dynamic vehicle systems suitable for the road conditions are adjusted. With vertical dynamic vehicle rule systems can depend on the nature of the system, for example Roadway the chassis control system are influenced so that at situations as uncritical as possible is selected while in critical driving situations and at the same time bad road properties present at the to implement safe, for example hard, chassis tuning is.

Claims (10)

1. System for obtaining a road surface represent the signal, especially for use in driving dynamics control, control and / or monitoring systems for passenger and utility vehicles, where

• - First signals (Xari (t) or Xari '(t)) are detected, which represent the relative movements between the vehicle body and at least one wheel, and
• - First means ( 101 ) are provided which, starting from the first signals (Xari (t) or Xari '(t)) form second signals (Si (t)), which represent the course of the road surface under the wheel rolling on it .

2. System according to claim 1, characterized in that second means ( 102 ) are provided which, starting from the second signals (Si (t)) form third signals (Ki (t)), which represent suitable parameters for the course of the road surface . 3. System according to at least one of the preceding claims, characterized in that third means ( 103 ) for classifying the third signals (Ki (t)) are provided. 4. System according to at least one of the preceding claims, characterized in that the dynamic driving control, control and / or monitoring systems ( 104 ) are adapted to the course of the road surface by the third signals (Ki (t)) and / or the classified third signals (Kli (t)) are taken into account in such a way that the controller parameters, for example coefficients and / or threshold values, controller setpoints and / or parts of the control algorithm of the vehicle dynamics control, control and / or monitoring systems are dependent can be changed by the third signals (Ki (t)) and / or the classified third signals (Kli (t)). 5. System according to at least one of the preceding claims, characterized in that as dynamic driving control, control and / or monitoring systems ( 104 )

• - Vertical dynamic vehicle control systems, such as chassis control systems, and / or
• - Horizontal dynamic vehicle control systems, such as systems for driving stability monitoring, for steering control, in particular for rear-axle steering control, for preventing blockage when braking and / or for traction control are available.

6. System according to at least one of the preceding claims, characterized in that a reduction of the information content of the second signals (Si (t)) takes place in the second means ( 102 ). 7. System according to at least one of the preceding claims, characterized in that in the third means ( 103 ) the classification of the third signals (Ki (t)) takes place such that the third signals (Ki (t)) with one or several thresholds are compared and, depending on the comparisons, the classified third signals (Kli (t)) are formed, the thresholds being fixed or being selected depending on variables which influence and / or represent the driving state. 8. System according to at least one of the preceding claims, characterized in that in order to reduce the information content of the second signals (Si (t)) in the second means ( 102 )

• - RMS values and / or peak values of the second signals (Si (t)) are determined in selectable time intervals and / or
• - Counting procedures for frequency determination within predetermined signal amplitude classes and selectable time intervals are carried out and / or
• - The spectral distribution of the signal amplitudes of the second signals (Si (t)) is determined as a function of the frequency within selectable time intervals, the time intervals being tracked in the sense of a sliding time window and the real time profile, and statistical characteristic values from this spectral distribution, such as The mean, scatter and effective value of the second signals (Si (t)) are determined and / or
• - Parameter identification methods are used, the time intervals being fixed or selected depending on variables that influence and / or represent the driving state.

9. System according to at least one of the preceding claims, since characterized in that in addition or alternatively for the first Signals (Xari (t) or Xari '(t)) fourth signals are detected the movements of at least one axis of the vehicle, for example represent the axis acceleration. 10. System according to at least one of the preceding claims, characterized in that the transmission behavior of the first means ( 101 ) is determined on the basis of a vehicle model which describes the vertical dynamics of a wheel unit of a vehicle, the transmission behavior of the first means ( 101 ) being determined, for example. in the Laplace area, where s is the usual Laplace variable, with can indicate if the first signals (Xari (t)) are present as input signals of the first means ( 101 ), which represent the spring deflection, or with can specify if the first signals (Xari '(t)) are present as input signals of the first means ( 101 ), which present the spring speed re.